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    Fast Nearest Neighbor Search in Medical Image Databases

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    TR_96-13.pdf (1.301Mb)
    No. of downloads: 1537

    Date
    1996
    Author
    Korn, Flip
    Sidiropoulos, N.
    Faloutsos, Christos
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    Abstract
    We examine the problem of finding similar tumor shapes. Starting from a natural similarity function (the so-called ax morphological distance'), we showed how to lower-bound it and how to search for nearest neighbors in large collections of tumor- like shapes.<P>Specifically, we used state-of-the-art concepts from morphology, namely the attern spectrum' of a shape, to map each shape to a point in n-dimensional space. Following [19, 36], we organized the n-d points in an R-tree. We showed that the L (= max) norm in the n-d space lower-bounds the actual distance. This guarantees no false dismissals for range queries. In addition, we developed a nearest neighbor algorithm that also guarantees no false dismissals.<P>Finally, we implemented the method, and we tested it against a testbed of realistic tumor shapes, using an established tumor-growth model of Murray Eden [15]. The experiments showed that our method is up to 27 times faster than straightforward sequential scanning.<P>
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    http://hdl.handle.net/1903/5743
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